[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126266-id":3,"doc-seo-126266-113":31,"detail-sidebar-cat-0-id-113":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126266,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",54,"Penelitian & Laporan","Penerapan Machine Learning Dalam Optimasi Proses Konversi Biomassa Menjadi Energi","Pemanfaatan biomassa sebagai energi terbarukan menjadi strategi penting untuk mendukung transisi ke sistem energi berkelanjutan. Penelitian ini mengevaluasi kinerja model machine learning (ML) dalam mengoptimasi proses konversi biomassa, khususnya pengarangan pelepah dan cangkang kelapa sawit, melalui prediksi parameter nilai kalor dan yield arang terhadap variasi suhu. Optimasi dilakukan pada 300–1000°C dengan waktu tinggal 2 jam, dianalisis menggunakan beberapa model ML. Penilaian performa memakai R², RMSE, dan MAE. Hasil menunjukkan GPR paling tinggi (R² mendekati 1) namun belum sepenuhnya merepresentasikan fenomena fisis proses pengarangan, sehingga pendekatan multi-model diperlukan untuk desain proses yang efisien dan berkelanjutan.","TK-002 p- ISSN : 2407 – 1846  \ne- ISSN : 2460 – 8416  \n[Website : jurnal.umj.ac.id/index.php/semnastek](Website : jurnal.umj.ac.id/index.php/semnastek)  \nPenerapan Machine Learning Dalam Optimasi Proses Konversi Biomassa  \nMenjadi Energi  \nAnnisa Vada Febriani1*, Murdifin2, M. Idris1, Budi Setya Wardhana1  \n1Magister Teknik Kimia, Fakultas Teknologi Industri, Universitas Ahmad Dahlan, Jl. Ringroad Selatan,  \nTamanan, Bantul, Yogyakarta, 55191  \n2 Magister Informatika, Fakultas Sains dan Teknologi, Universitas Islam Negeri Sunan Kalijaga, Jl. Marsda Adisucipto, Yogyakarta, 55281  \n*Corresponding Author: [2307054003@webmail.uad.ac.id](2307054003@webmail.uad.ac.id)  \nAbstrak  \nPemanfaatan biomassa sebagai sumber energi terbarukan menjadi strategi penting dalam mendukung transisi menuju sistem energi berkelanjutan. Penelitian ini bertujuan untuk mengevaluasi kinerja model machine learning (ML) dalam mengoptimasi proses konversi biomassa, khususnya pada pengarangan pelepah dan cangkang kelapa sawit, melalui prediksi parameter nilai kalor dan Yield arang terhadap variasi suhu. Optimasi proses pengarangan dilakukan pada suhu 300°Chingga 1000°C dengan waktu tinggal 2 jam, dan hasilnya dianalisis menggunakan beberapa model ML. Evaluasi performa model dilakukan berdasarkan nilai koefisiendeterminasi (R²), Root Mean Square Error (RMSE), dan Mean Absolute Error (MAE) . Hasil menunjukkan bahwa model GPR memiliki performa prediksi tertinggi dengan nilai R² mendekati 1, namun GPR cenderung menghasilkan prediksi yang tidak sepenuhnya sesuai dengan fenomena fisis proses pengarangan. Studi ini menekankan pentingnya pendekatan multi-model dalam optimasi konversi biomassadan menunjukkan bahwa pemilihan model ML tidak hanya bergantung pada akurasi prediktif, tetapi juga pada kemampuan model merepresentasikan mekanisme proses yang mendasari. Hasil penelitian ini memberikan kontribusi terhadap pengembangan sistem cerdas dalam perancangan proses konversi biomassa secara efisien dan berkelanjutan.  \nKata kunci: Biomassa; Energi; Machine Learning; Kelapa sawit; Optimasi  \nAbstract  \nThe utilisation of biomass as a renewable energy source is an important strategy in supporting the transition to a sustainable energy system. This study aims to evaluate the performance of machine learning (ML) models in optimising the biomass conversion process, especially in the charring of palm fronds and shells, through prediction of heating value parameters and charcoal Yield against temperature variations. Optimisation of the charring process was carried out at temperatures of 300°C to 1000°C with a residence time of 2 hours, and the results were analysed using several ML models. Model performance evaluation was conducted based on the coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) . The results show that the GPR model has the highest prediction performance with R² values close to 1, but GPR tends to produce predictions that do not fully match the physical phenomena of the charring process.  \nThis study emphasises the importance of multi-model approach in biomass conversion optimisation and shows that the selection of ML model depends not only on the predictive accuracy, but also on the ability of the model to represent the underlying process mechanism. The results of this study contribute to the development of intelligent systems in the design of efficient and sustainable biomass conversion processes.  \nKeywords : Biomass; Energy; Machine Learning; Oil palm; Optimisation  \nSeminar Nasional Sains dan Teknologi 2025 1  \nFakultas Teknik Universitas Muhammadiyah Jakarta, 28 Mei 2025  \nTK-002 p- ISSN : 2407 – 1846  \ne- ISSN : 2460 – 8416  \n[Website : jurnal.umj.ac.id/index.php/semnastek](Website : jurnal.umj.ac.id/index.php/semnastek)  \nPENDAHULUAN  \nKebutuhan global akan energi bersih, efisien, dan berkelanjutan semakin mendesak seiring meningkatnya dampak negatif penggunaan energi fosil terhadap lingkungan. Hal ini mendoro","cbCaiqhLP0PbKcL7","https://ap.wps.com/l/cbCaiqhLP0PbKcL7","pdf",360525,5,1,12,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang energi terbarukan dan biomassa\n## Metode termokimia dan kebutuhan model optimasi\n## Peran machine learning dalam optimasi konversi termal","[{\"question\":\"Penelitian ini mengevaluasi kinerja model ML untuk tujuan apa?\",\"answer\":\"Untuk mengevaluasi kinerja model machine learning dalam mengoptimasi proses konversi biomassa melalui prediksi parameter nilai kalor dan yield arang terhadap variasi suhu.\"},{\"question\":\"Bagaimana kondisi proses pengarangan dalam optimasi yang dilakukan?\",\"answer\":\"Pengarangan dilakukan pada rentang suhu 300°C hingga 1000°C dengan waktu tinggal 2 jam.\"},{\"question\":\"Model ML apa yang menunjukkan performa prediksi tertinggi dan indikatornya apa?\",\"answer\":\"Model GPR menunjukkan performa prediksi tertinggi dengan nilai R² mendekati 1, dievaluasi menggunakan R², RMSE, dan MAE.\"}]","Penerapan Machine Learning Dalam Optimasi Proses Konversi Biomassa Menjadi Energi | PDF",1785904141,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"application-of-machine-learning-in-optimizing-biomass-conversion-process-into-energy","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/application-of-machine-learning-in-optimizing-biomass-conversion-process-into-energy/126266/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-17","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Penelitian ini mengevaluasi kinerja model ML untuk tujuan apa?","Question",{"text":77,"@type":78},"Untuk mengevaluasi kinerja model machine learning dalam mengoptimasi proses konversi biomassa melalui prediksi parameter nilai kalor dan yield arang terhadap variasi suhu.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Bagaimana kondisi proses pengarangan dalam optimasi yang dilakukan?",{"text":82,"@type":78},"Pengarangan dilakukan pada rentang suhu 300°C hingga 1000°C dengan waktu tinggal 2 jam.",{"name":84,"@type":75,"acceptedAnswer":85},"Model ML apa yang menunjukkan performa prediksi tertinggi dan indikatornya apa?",{"text":86,"@type":78},"Model GPR menunjukkan performa prediksi tertinggi dengan nilai R² mendekati 1, dievaluasi menggunakan R², RMSE, dan MAE.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]